Overview

Dataset statistics

Number of variables38
Number of observations29786
Missing cells119359
Missing cells (%)10.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 MiB
Average record size in memory269.0 B

Variable types

Numeric10
Categorical21
Boolean7

Alerts

disable_communication has constant value "False" Constant
friends has constant value "[]" Constant
is_backing has constant value "False" Constant
is_starred has constant value "False" Constant
permissions has constant value "[]" Constant
blurb has a high cardinality: 29521 distinct values High cardinality
created_at has a high cardinality: 29784 distinct values High cardinality
creator has a high cardinality: 25247 distinct values High cardinality
deadline has a high cardinality: 29096 distinct values High cardinality
launched_at has a high cardinality: 29761 distinct values High cardinality
name has a high cardinality: 29751 distinct values High cardinality
slug has a high cardinality: 29786 distinct values High cardinality
state_changed_at has a high cardinality: 29202 distinct values High cardinality
location_city has a high cardinality: 4279 distinct values High cardinality
location_state has a high cardinality: 621 distinct values High cardinality
backers_count is highly correlated with converted_pledged_amount and 3 other fieldsHigh correlation
converted_pledged_amount is highly correlated with backers_count and 3 other fieldsHigh correlation
currency_trailing_code is highly correlated with fx_rate and 2 other fieldsHigh correlation
fx_rate is highly correlated with currency_trailing_code and 2 other fieldsHigh correlation
pledged is highly correlated with backers_count and 3 other fieldsHigh correlation
spotlight is highly correlated with backers_count and 3 other fieldsHigh correlation
static_usd_rate is highly correlated with currency_trailing_code and 2 other fieldsHigh correlation
usd_exchange_rate is highly correlated with currency_trailing_code and 2 other fieldsHigh correlation
usd_pledged is highly correlated with backers_count and 3 other fieldsHigh correlation
backers_count is highly correlated with converted_pledged_amount and 1 other fieldsHigh correlation
converted_pledged_amount is highly correlated with backers_count and 2 other fieldsHigh correlation
currency_trailing_code is highly correlated with fx_rate and 2 other fieldsHigh correlation
fx_rate is highly correlated with currency_trailing_code and 2 other fieldsHigh correlation
pledged is highly correlated with converted_pledged_amount and 1 other fieldsHigh correlation
static_usd_rate is highly correlated with currency_trailing_code and 2 other fieldsHigh correlation
usd_exchange_rate is highly correlated with currency_trailing_code and 2 other fieldsHigh correlation
usd_pledged is highly correlated with backers_count and 2 other fieldsHigh correlation
backers_count is highly correlated with converted_pledged_amount and 3 other fieldsHigh correlation
converted_pledged_amount is highly correlated with backers_count and 3 other fieldsHigh correlation
currency_trailing_code is highly correlated with fx_rate and 2 other fieldsHigh correlation
fx_rate is highly correlated with currency_trailing_code and 2 other fieldsHigh correlation
pledged is highly correlated with backers_count and 2 other fieldsHigh correlation
spotlight is highly correlated with backers_count and 2 other fieldsHigh correlation
static_usd_rate is highly correlated with currency_trailing_code and 2 other fieldsHigh correlation
usd_exchange_rate is highly correlated with currency_trailing_code and 2 other fieldsHigh correlation
usd_pledged is highly correlated with backers_count and 3 other fieldsHigh correlation
currency is highly correlated with is_starred and 8 other fieldsHigh correlation
is_starred is highly correlated with currency and 16 other fieldsHigh correlation
usd_type is highly correlated with is_starred and 5 other fieldsHigh correlation
spotlight is highly correlated with is_starred and 7 other fieldsHigh correlation
currency_trailing_code is highly correlated with currency and 8 other fieldsHigh correlation
is_starrable is highly correlated with is_starred and 5 other fieldsHigh correlation
source_url is highly correlated with is_starred and 6 other fieldsHigh correlation
friends is highly correlated with currency and 16 other fieldsHigh correlation
currency_symbol is highly correlated with currency and 8 other fieldsHigh correlation
current_currency is highly correlated with is_starred and 5 other fieldsHigh correlation
permissions is highly correlated with currency and 16 other fieldsHigh correlation
country_displayable_name is highly correlated with currency and 8 other fieldsHigh correlation
is_backing is highly correlated with currency and 16 other fieldsHigh correlation
category is highly correlated with is_starred and 6 other fieldsHigh correlation
country is highly correlated with currency and 8 other fieldsHigh correlation
disable_communication is highly correlated with currency and 16 other fieldsHigh correlation
state is highly correlated with is_starred and 6 other fieldsHigh correlation
staff_pick is highly correlated with is_starred and 4 other fieldsHigh correlation
backers_count is highly correlated with converted_pledged_amount and 2 other fieldsHigh correlation
category is highly correlated with is_starrable and 3 other fieldsHigh correlation
converted_pledged_amount is highly correlated with backers_count and 2 other fieldsHigh correlation
country is highly correlated with country_displayable_name and 6 other fieldsHigh correlation
country_displayable_name is highly correlated with country and 6 other fieldsHigh correlation
currency is highly correlated with country and 6 other fieldsHigh correlation
currency_symbol is highly correlated with country and 6 other fieldsHigh correlation
currency_trailing_code is highly correlated with country and 6 other fieldsHigh correlation
fx_rate is highly correlated with country and 6 other fieldsHigh correlation
is_starrable is highly correlated with category and 1 other fieldsHigh correlation
pledged is highly correlated with backers_count and 2 other fieldsHigh correlation
source_url is highly correlated with category and 2 other fieldsHigh correlation
spotlight is highly correlated with category and 2 other fieldsHigh correlation
state is highly correlated with category and 3 other fieldsHigh correlation
static_usd_rate is highly correlated with country and 6 other fieldsHigh correlation
usd_exchange_rate is highly correlated with country and 6 other fieldsHigh correlation
usd_pledged is highly correlated with backers_count and 2 other fieldsHigh correlation
friends has 29774 (> 99.9%) missing values Missing
is_backing has 29774 (> 99.9%) missing values Missing
is_starred has 29774 (> 99.9%) missing values Missing
permissions has 29774 (> 99.9%) missing values Missing
backers_count is highly skewed (γ1 = 35.14232734) Skewed
converted_pledged_amount is highly skewed (γ1 = 35.5152626) Skewed
goal is highly skewed (γ1 = 85.34983856) Skewed
pledged is highly skewed (γ1 = 40.96339002) Skewed
usd_pledged is highly skewed (γ1 = 35.523334) Skewed
blurb is uniformly distributed Uniform
created_at is uniformly distributed Uniform
creator is uniformly distributed Uniform
deadline is uniformly distributed Uniform
launched_at is uniformly distributed Uniform
name is uniformly distributed Uniform
slug is uniformly distributed Uniform
state_changed_at is uniformly distributed Uniform
id has unique values Unique
slug has unique values Unique
backers_count has 1674 (5.6%) zeros Zeros
converted_pledged_amount has 1783 (6.0%) zeros Zeros
pledged has 1674 (5.6%) zeros Zeros
usd_pledged has 1674 (5.6%) zeros Zeros

Reproduction

Analysis started2022-03-04 18:21:20.357554
Analysis finished2022-03-04 18:21:50.507254
Duration30.15 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

Distinct3666
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1775.418049
Minimum0
Maximum3665
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size232.8 KiB
2022-03-04T13:21:50.623448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile172
Q1865
median1736
Q32683
95-th percentile3463.75
Maximum3665
Range3665
Interquartile range (IQR)1818

Descriptive statistics

Standard deviation1053.451646
Coefficient of variation (CV)0.5933541381
Kurtosis-1.185328284
Mean1775.418049
Median Absolute Deviation (MAD)907
Skewness0.07235547447
Sum52882602
Variance1109760.371
MonotonicityNot monotonic
2022-03-04T13:21:50.765676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09
 
< 0.1%
11649
 
< 0.1%
11779
 
< 0.1%
11759
 
< 0.1%
11739
 
< 0.1%
11719
 
< 0.1%
11709
 
< 0.1%
11689
 
< 0.1%
11679
 
< 0.1%
11669
 
< 0.1%
Other values (3656)29696
99.7%
ValueCountFrequency (%)
09
< 0.1%
18
< 0.1%
28
< 0.1%
39
< 0.1%
49
< 0.1%
58
< 0.1%
69
< 0.1%
78
< 0.1%
89
< 0.1%
99
< 0.1%
ValueCountFrequency (%)
36651
 
< 0.1%
36641
 
< 0.1%
36631
 
< 0.1%
36621
 
< 0.1%
36612
 
< 0.1%
36603
< 0.1%
36592
 
< 0.1%
36583
< 0.1%
36574
< 0.1%
36565
< 0.1%

backers_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1549
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.9706238
Minimum0
Maximum91585
Zeros1674
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size232.8 KiB
2022-03-04T13:21:50.914626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median37
Q3107
95-th percentile548
Maximum91585
Range91585
Interquartile range (IQR)99

Descriptive statistics

Standard deviation1252.665749
Coefficient of variation (CV)6.92192867
Kurtosis1763.673488
Mean180.9706238
Median Absolute Deviation (MAD)34
Skewness35.14232734
Sum5390391
Variance1569171.479
MonotonicityNot monotonic
2022-03-04T13:21:51.036896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01674
 
5.6%
11596
 
5.4%
21152
 
3.9%
3803
 
2.7%
4618
 
2.1%
5508
 
1.7%
6431
 
1.4%
8400
 
1.3%
7387
 
1.3%
10333
 
1.1%
Other values (1539)21884
73.5%
ValueCountFrequency (%)
01674
5.6%
11596
5.4%
21152
3.9%
3803
2.7%
4618
 
2.1%
5508
 
1.7%
6431
 
1.4%
7387
 
1.3%
8400
 
1.3%
9304
 
1.0%
ValueCountFrequency (%)
915851
< 0.1%
634161
< 0.1%
585611
< 0.1%
572091
< 0.1%
465201
< 0.1%
451621
< 0.1%
414861
< 0.1%
374011
< 0.1%
363741
< 0.1%
314631
< 0.1%

blurb
Categorical

HIGH CARDINALITY
UNIFORM

Distinct29521
Distinct (%)99.1%
Missing2
Missing (%)< 0.1%
Memory size232.8 KiB
A beautiful natural Fine art nude book exemplifying the female form presented by female producer Nina Vain.
 
14
Crappy Custom Postcard Drawings ...and poetry too!
 
8
Firedance Jewelry pieces are uniquely handcrafted using fused dichroic glass. Firedance Jewelry - the fusion of art & science.
 
8
A couple's love story is portrayed by 100 actors who compete for the 2 lead roles and the chance to perform the final terrifying scene.
 
6
Wearing masks to express our inner feelings and desires.
 
6
Other values (29516)
29742 

Length

Max length196
Median length119
Mean length105.9437618
Min length1

Characters and Unicode

Total characters1467
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29326 ?
Unique (%)98.5%

Sample

1st rowA super-cheeky strategy card game that's full of monsters, magic and lots of sneaky revenge!
2nd rowHelp us finish a feature film about two strangers stuck in adjacent elevators that share a conversation that may alter their realities.
3rd rowConsidered playfully mischievous and outlaws in the world of movement, Rogue Co. brings you their 5th full length production, "PHASE."
4th rowA fantastical love story about two New Yorkers whose relationship unfolds aboard a paper mache subway!
5th rowA story about a girl named Jane who is learning to accept human frailty as she deals with the news of her sister getting knocked up.

Common Values

ValueCountFrequency (%)
A beautiful natural Fine art nude book exemplifying the female form presented by female producer Nina Vain.14
 
< 0.1%
Crappy Custom Postcard Drawings ...and poetry too!8
 
< 0.1%
Firedance Jewelry pieces are uniquely handcrafted using fused dichroic glass. Firedance Jewelry - the fusion of art & science.8
 
< 0.1%
A couple's love story is portrayed by 100 actors who compete for the 2 lead roles and the chance to perform the final terrifying scene.6
 
< 0.1%
Wearing masks to express our inner feelings and desires.6
 
< 0.1%
Beautiful girls – models, shows their nude photos Militancy and tender, erotic and at the same time inaccessible ...5
 
< 0.1%
Hard Enamel Pins5
 
< 0.1%
( WARNING : This photo issue may contain traces of nudity. ) Printed , eBook5
 
< 0.1%
A meeting of performing and creative artists, educators and producers working across artistic disciplines and cultural divides4
 
< 0.1%
cool dog is a good dog. he is best conveyed in sticker form. people love stickers. he is to be shared with all the people of the world.4
 
< 0.1%
Other values (29511)29719
99.8%

Length

2022-03-04T13:21:51.459142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a21380
 
4.1%
the20483
 
3.9%
and16074
 
3.1%
of15739
 
3.0%
to12788
 
2.4%
in8219
 
1.6%
for5934
 
1.1%
is5452
 
1.0%
with5009
 
1.0%
4922
 
0.9%
Other values (43887)406699
77.8%

Most occurring characters

ValueCountFrequency (%)
1467
100.0%

Most occurring categories

ValueCountFrequency (%)
Control1467
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
1467
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1467
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1467
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1467
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1467
100.0%

category
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size232.8 KiB
Graphic Novels
2400 
Classical Music
2400 
Narrative Film
2400 
Jewelry
1998 
Wearables
1898 
Other values (39)
18690 

Length

Max length16
Median length12
Mean length10.89531995
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTabletop Games
2nd rowNarrative Film
3rd rowPerformances
4th rowRomance
5th rowNarrative Film

Common Values

ValueCountFrequency (%)
Graphic Novels2400
 
8.1%
Classical Music2400
 
8.1%
Narrative Film2400
 
8.1%
Jewelry1998
 
6.7%
Wearables1898
 
6.4%
Poetry1816
 
6.1%
Tabletop Games1719
 
5.8%
Dance1367
 
4.6%
Radio & Podcasts1306
 
4.4%
Performances1293
 
4.3%
Other values (34)11189
37.6%

Length

2022-03-04T13:21:51.591220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
art3570
 
7.7%
graphic2400
 
5.2%
classical2400
 
5.2%
music2400
 
5.2%
narrative2400
 
5.2%
film2400
 
5.2%
novels2400
 
5.2%
games2096
 
4.5%
jewelry1998
 
4.3%
wearables1898
 
4.1%
Other values (43)22283
48.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

converted_pledged_amount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct12468
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15811.01847
Minimum0
Maximum8596474
Zeros1783
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size232.8 KiB
2022-03-04T13:21:51.709644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1351
median2268
Q37713.25
95-th percentile43242
Maximum8596474
Range8596474
Interquartile range (IQR)7362.25

Descriptive statistics

Standard deviation132935.8178
Coefficient of variation (CV)8.407796
Kurtosis1701.172889
Mean15811.01847
Median Absolute Deviation (MAD)2227
Skewness35.5152626
Sum470946996
Variance1.767193165 × 1010
MonotonicityNot monotonic
2022-03-04T13:21:51.831936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01783
 
6.0%
1664
 
2.2%
2149
 
0.5%
10129
 
0.4%
5103
 
0.3%
5095
 
0.3%
2595
 
0.3%
686
 
0.3%
10077
 
0.3%
371
 
0.2%
Other values (12458)26534
89.1%
ValueCountFrequency (%)
01783
6.0%
1664
 
2.2%
2149
 
0.5%
371
 
0.2%
430
 
0.1%
5103
 
0.3%
686
 
0.3%
730
 
0.1%
833
 
0.1%
916
 
0.1%
ValueCountFrequency (%)
85964741
< 0.1%
83247921
< 0.1%
59880891
< 0.1%
57021531
< 0.1%
53337921
< 0.1%
48319751
< 0.1%
48091041
< 0.1%
46816361
< 0.1%
35029601
< 0.1%
33108721
< 0.1%

country
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size232.8 KiB
US
20551 
GB
3404 
CA
 
1429
AU
 
624
DE
 
561
Other values (20)
3217 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAU
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US20551
69.0%
GB3404
 
11.4%
CA1429
 
4.8%
AU624
 
2.1%
DE561
 
1.9%
FR502
 
1.7%
IT437
 
1.5%
ES377
 
1.3%
MX341
 
1.1%
NL265
 
0.9%
Other values (15)1295
 
4.3%

Length

2022-03-04T13:21:51.950062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us20551
69.0%
gb3404
 
11.4%
ca1429
 
4.8%
au624
 
2.1%
de561
 
1.9%
fr502
 
1.7%
it437
 
1.5%
es377
 
1.3%
mx341
 
1.1%
nl265
 
0.9%
Other values (15)1295
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

country_displayable_name
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size232.8 KiB
the United States
20551 
the United Kingdom
3404 
Canada
 
1429
Australia
 
624
Germany
 
561
Other values (20)
3217 

Length

Max length18
Median length17
Mean length15.16907272
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAustralia
2nd rowthe United States
3rd rowthe United States
4th rowthe United States
5th rowthe United States

Common Values

ValueCountFrequency (%)
the United States20551
69.0%
the United Kingdom3404
 
11.4%
Canada1429
 
4.8%
Australia624
 
2.1%
Germany561
 
1.9%
France502
 
1.7%
Italy437
 
1.5%
Spain377
 
1.3%
Mexico341
 
1.1%
the Netherlands265
 
0.9%
Other values (15)1295
 
4.3%

Length

2022-03-04T13:21:52.046472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the24220
31.0%
united23955
30.6%
states20551
26.3%
kingdom3404
 
4.4%
canada1429
 
1.8%
australia624
 
0.8%
germany561
 
0.7%
france502
 
0.6%
italy437
 
0.6%
spain377
 
0.5%
Other values (19)2179
 
2.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

created_at
Categorical

HIGH CARDINALITY
UNIFORM

Distinct29784
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size232.8 KiB
2018-07-12 18:26:53
 
2
2015-10-16 16:41:53
 
2
2021-05-21 01:00:51
 
1
2018-09-15 19:09:59
 
1
2015-09-28 20:42:30
 
1
Other values (29779)
29779 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29782 ?
Unique (%)> 99.9%

Sample

1st row2021-05-21 01:00:51
2nd row2012-01-26 18:20:01
3rd row2015-11-13 21:04:33
4th row2013-02-01 17:22:07
5th row2012-02-23 19:45:00

Common Values

ValueCountFrequency (%)
2018-07-12 18:26:532
 
< 0.1%
2015-10-16 16:41:532
 
< 0.1%
2021-05-21 01:00:511
 
< 0.1%
2018-09-15 19:09:591
 
< 0.1%
2015-09-28 20:42:301
 
< 0.1%
2011-10-10 21:55:071
 
< 0.1%
2019-05-13 15:19:211
 
< 0.1%
2014-08-25 21:44:211
 
< 0.1%
2019-05-11 09:58:001
 
< 0.1%
2016-11-28 15:57:131
 
< 0.1%
Other values (29774)29774
> 99.9%

Length

2022-03-04T13:21:52.144959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2014-07-0862
 
0.1%
2021-01-2545
 
0.1%
2021-02-0844
 
0.1%
2014-07-0944
 
0.1%
2021-01-1444
 
0.1%
2021-01-2643
 
0.1%
2021-02-2240
 
0.1%
2021-01-2740
 
0.1%
2021-01-1139
 
0.1%
2021-02-0139
 
0.1%
Other values (28604)59132
99.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

creator
Categorical

HIGH CARDINALITY
UNIFORM

Distinct25247
Distinct (%)84.9%
Missing64
Missing (%)0.2%
Memory size232.8 KiB
Mary & Josh
 
29
Evolutionary Comics
 
26
Romeo Press
 
23
NightstormWest (deleted)
 
22
A K Nicholas
 
20
Other values (25242)
29602 

Length

Max length50
Median length13
Mean length14.48018303
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22548 ?
Unique (%)75.9%

Sample

1st rowJof Croxford
2nd rowNine Finger Productions
3rd rowRogue Co.
4th rowRyan Mitchel and Team
5th rowCarol Brandt

Common Values

ValueCountFrequency (%)
Mary & Josh29
 
0.1%
Evolutionary Comics26
 
0.1%
Romeo Press23
 
0.1%
NightstormWest (deleted)22
 
0.1%
A K Nicholas20
 
0.1%
badgirlartwork.com17
 
0.1%
Megan Tilton17
 
0.1%
Pat Shand16
 
0.1%
Adam Reuter14
 
< 0.1%
Curt13
 
< 0.1%
Other values (25237)29525
99.1%
(Missing)64
 
0.2%

Length

2022-03-04T13:21:52.269773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
763
 
1.2%
deleted552
 
0.8%
the499
 
0.8%
and434
 
0.7%
dance408
 
0.6%
david324
 
0.5%
michael316
 
0.5%
games303
 
0.5%
john247
 
0.4%
james197
 
0.3%
Other values (23771)62303
93.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

currency
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size232.8 KiB
USD
20551 
GBP
3404 
EUR
2505 
CAD
 
1429
AUD
 
624
Other values (10)
 
1273

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAUD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD20551
69.0%
GBP3404
 
11.4%
EUR2505
 
8.4%
CAD1429
 
4.8%
AUD624
 
2.1%
MXN341
 
1.1%
SEK205
 
0.7%
HKD195
 
0.7%
SGD119
 
0.4%
CHF92
 
0.3%
Other values (5)321
 
1.1%

Length

2022-03-04T13:21:52.384247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usd20551
69.0%
gbp3404
 
11.4%
eur2505
 
8.4%
cad1429
 
4.8%
aud624
 
2.1%
mxn341
 
1.1%
sek205
 
0.7%
hkd195
 
0.7%
sgd119
 
0.4%
chf92
 
0.3%
Other values (5)321
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

currency_symbol
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.8 KiB
$
23342 
£
3404 
2505 
kr
 
352
Fr
 
92
Other values (2)
 
91

Length

Max length3
Median length1
Mean length1.018397905
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$
2nd row$
3rd row$
4th row$
5th row$

Common Values

ValueCountFrequency (%)
$23342
78.4%
£3404
 
11.4%
2505
 
8.4%
kr352
 
1.2%
Fr 92
 
0.3%
¥79
 
0.3%
12
 
< 0.1%

Length

2022-03-04T13:21:52.483361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-04T13:21:52.566935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
23342
78.4%
£3404
 
11.4%
2505
 
8.4%
kr352
 
1.2%
fr92
 
0.3%
¥79
 
0.3%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

currency_trailing_code
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
True
23694 
False
6092 
ValueCountFrequency (%)
True23694
79.5%
False6092
 
20.5%
2022-03-04T13:21:52.628861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

current_currency
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.8 KiB
USD
29703 
CAD
 
83

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD29703
99.7%
CAD83
 
0.3%

Length

2022-03-04T13:21:52.695290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-04T13:21:52.762449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
usd29703
99.7%
cad83
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

deadline
Categorical

HIGH CARDINALITY
UNIFORM

Distinct29096
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size232.8 KiB
2021-07-01 03:59:00
 
9
2021-05-01 03:59:00
 
8
2021-01-01 04:59:00
 
6
2021-02-28 23:59:00
 
5
2021-03-01 04:59:00
 
5
Other values (29091)
29753 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28521 ?
Unique (%)95.8%

Sample

1st row2021-07-14 02:00:04
2nd row2012-04-02 06:59:00
3rd row2016-02-13 18:34:51
4th row2013-03-14 12:18:28
5th row2012-04-01 04:59:00

Common Values

ValueCountFrequency (%)
2021-07-01 03:59:009
 
< 0.1%
2021-05-01 03:59:008
 
< 0.1%
2021-01-01 04:59:006
 
< 0.1%
2021-02-28 23:59:005
 
< 0.1%
2021-03-01 04:59:005
 
< 0.1%
2021-05-01 01:00:005
 
< 0.1%
2021-03-25 22:00:005
 
< 0.1%
2015-05-01 03:59:004
 
< 0.1%
2019-01-01 04:59:004
 
< 0.1%
2021-02-01 04:59:004
 
< 0.1%
Other values (29086)29731
99.8%

Length

2022-03-04T13:21:52.832172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
03:59:00734
 
1.2%
04:59:00632
 
1.1%
04:00:00404
 
0.7%
06:59:00353
 
0.6%
00:00:00348
 
0.6%
01:00:00326
 
0.5%
02:00:00315
 
0.5%
03:00:00307
 
0.5%
22:00:00271
 
0.5%
05:00:00266
 
0.4%
Other values (21899)55616
93.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

disable_communication
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
False
29786 
ValueCountFrequency (%)
False29786
100.0%
2022-03-04T13:21:52.895065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

friends
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)8.3%
Missing29774
Missing (%)> 99.9%
Memory size232.8 KiB
[]
12 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[]
5th row[]

Common Values

ValueCountFrequency (%)
[]12
 
< 0.1%
(Missing)29774
> 99.9%

Length

2022-03-04T13:21:52.961962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-04T13:21:53.026337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
12
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fx_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.018223792
Minimum0.00903163
Maximum1.71773213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.8 KiB
2022-03-04T13:21:53.096128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.00903163
5-th percentile0.76265492
Q11
median1
Q31
95-th percentile1.39919434
Maximum1.71773213
Range1.7087005
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2282321839
Coefficient of variation (CV)0.2241473689
Kurtosis7.057249031
Mean1.018223792
Median Absolute Deviation (MAD)0
Skewness-1.687713594
Sum30328.81386
Variance0.05208992976
MonotonicityNot monotonic
2022-03-04T13:21:53.214522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
120520
68.9%
1.399194342967
 
10.0%
1.199377282087
 
7.0%
0.81455911174
 
3.9%
0.76265492522
 
1.8%
1.40820476429
 
1.4%
1.21227991395
 
1.3%
0.04911233280
 
0.9%
0.82118999251
 
0.8%
0.1183029155
 
0.5%
Other values (30)1006
 
3.4%
ValueCountFrequency (%)
0.0090316371
 
0.2%
0.009082326
 
< 0.1%
0.011087752
 
< 0.1%
0.04911233280
0.9%
0.0499351558
 
0.2%
0.060293153
 
< 0.1%
0.1181686647
 
0.2%
0.1183029155
0.5%
0.1200686949
 
0.2%
0.1202771410
 
< 0.1%
ValueCountFrequency (%)
1.717732138
 
< 0.1%
1.4724251223
 
0.1%
1.40820476429
 
1.4%
1.399194342967
 
10.0%
1.2276580135
 
0.1%
1.21227991395
 
1.3%
1.199377282087
 
7.0%
1.1124571817
 
0.1%
1.1004707875
 
0.3%
120520
68.9%

goal
Real number (ℝ≥0)

SKEWED

Distinct1706
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36400.98363
Minimum0.01
Maximum100000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.8 KiB
2022-03-04T13:21:53.349823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile250
Q11000
median3900
Q310000
95-th percentile60000
Maximum100000000
Range99999999.99
Interquartile range (IQR)9000

Descriptive statistics

Standard deviation788100.9674
Coefficient of variation (CV)21.65054042
Kurtosis9488.816589
Mean36400.98363
Median Absolute Deviation (MAD)3300
Skewness85.34983856
Sum1084239698
Variance6.211031348 × 1011
MonotonicityNot monotonic
2022-03-04T13:21:53.483181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50001931
 
6.5%
100001470
 
4.9%
10001448
 
4.9%
5001378
 
4.6%
30001304
 
4.4%
20001252
 
4.2%
1500894
 
3.0%
2500890
 
3.0%
15000837
 
2.8%
20000719
 
2.4%
Other values (1696)17663
59.3%
ValueCountFrequency (%)
0.011
 
< 0.1%
164
0.2%
22
 
< 0.1%
31
 
< 0.1%
42
 
< 0.1%
510
 
< 0.1%
71
 
< 0.1%
83
 
< 0.1%
1049
0.2%
124
 
< 0.1%
ValueCountFrequency (%)
1000000001
 
< 0.1%
500000001
 
< 0.1%
330000001
 
< 0.1%
250000003
< 0.1%
200000003
< 0.1%
125000001
 
< 0.1%
100000007
< 0.1%
90000003
< 0.1%
85471001
 
< 0.1%
75000001
 
< 0.1%

id
Real number (ℝ≥0)

UNIQUE

Distinct29786
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1066745262
Minimum53154
Maximum2147466649
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.8 KiB
2022-03-04T13:21:53.619974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum53154
5-th percentile104029011.8
Q1523759718.2
median1061726379
Q31606513003
95-th percentile2037708346
Maximum2147466649
Range2147413495
Interquartile range (IQR)1082753285

Descriptive statistics

Standard deviation623176990.8
Coefficient of variation (CV)0.5841853845
Kurtosis-1.218186352
Mean1066745262
Median Absolute Deviation (MAD)540755953
Skewness0.01193683368
Sum3.177407436 × 1013
Variance3.883495618 × 1017
MonotonicityNot monotonic
2022-03-04T13:21:53.751195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21288672361
 
< 0.1%
12049826481
 
< 0.1%
16317200781
 
< 0.1%
7972738751
 
< 0.1%
3528578971
 
< 0.1%
25046701
 
< 0.1%
18175582481
 
< 0.1%
13808579631
 
< 0.1%
21424981361
 
< 0.1%
21136020141
 
< 0.1%
Other values (29776)29776
> 99.9%
ValueCountFrequency (%)
531541
< 0.1%
1132301
< 0.1%
1278001
< 0.1%
1711161
< 0.1%
2748651
< 0.1%
2855831
< 0.1%
3258751
< 0.1%
3798731
< 0.1%
5464551
< 0.1%
7311941
< 0.1%
ValueCountFrequency (%)
21474666491
< 0.1%
21474601191
< 0.1%
21474305991
< 0.1%
21474167471
< 0.1%
21473803161
< 0.1%
21473647811
< 0.1%
21473394831
< 0.1%
21473367471
< 0.1%
21471805461
< 0.1%
21470347661
< 0.1%

is_backing
Boolean

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)8.3%
Missing29774
Missing (%)> 99.9%
Memory size232.8 KiB
False
 
12
(Missing)
29774 
ValueCountFrequency (%)
False12
 
< 0.1%
(Missing)29774
> 99.9%
2022-03-04T13:21:53.844615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

is_starrable
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
False
28617 
True
 
1169
ValueCountFrequency (%)
False28617
96.1%
True1169
 
3.9%
2022-03-04T13:21:53.877182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

is_starred
Boolean

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)8.3%
Missing29774
Missing (%)> 99.9%
Memory size232.8 KiB
False
 
12
(Missing)
29774 
ValueCountFrequency (%)
False12
 
< 0.1%
(Missing)29774
> 99.9%
2022-03-04T13:21:53.913165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

launched_at
Categorical

HIGH CARDINALITY
UNIFORM

Distinct29761
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size232.8 KiB
2021-03-16 14:00:26
 
2
2020-07-02 16:00:06
 
2
2018-10-15 14:00:01
 
2
2021-05-11 09:00:01
 
2
2021-03-01 17:00:01
 
2
Other values (29756)
29776 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29736 ?
Unique (%)99.8%

Sample

1st row2021-06-14 02:00:04
2nd row2012-02-12 20:00:26
3rd row2016-01-14 18:34:51
4th row2013-02-12 13:18:28
5th row2012-03-02 13:20:47

Common Values

ValueCountFrequency (%)
2021-03-16 14:00:262
 
< 0.1%
2020-07-02 16:00:062
 
< 0.1%
2018-10-15 14:00:012
 
< 0.1%
2021-05-11 09:00:012
 
< 0.1%
2021-03-01 17:00:012
 
< 0.1%
2021-02-16 16:00:022
 
< 0.1%
2021-03-30 17:00:072
 
< 0.1%
2021-02-12 20:00:012
 
< 0.1%
2021-02-02 19:00:032
 
< 0.1%
2021-02-23 16:00:002
 
< 0.1%
Other values (29751)29766
99.9%

Length

2022-03-04T13:21:53.974720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-06-01131
 
0.2%
2021-02-0194
 
0.2%
2021-02-0292
 
0.2%
2021-06-1591
 
0.2%
2021-02-2372
 
0.1%
2021-05-1871
 
0.1%
2021-06-0871
 
0.1%
2021-02-1671
 
0.1%
2021-05-2568
 
0.1%
2021-03-2366
 
0.1%
Other values (27522)58745
98.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct29751
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size232.8 KiB
Home
 
2
Car Wash (Canceled)
 
2
The Bigger Picture
 
2
APEX
 
2
UTouch: Your protection against germs on public touchscreens (Canceled)
 
2
Other values (29746)
29776 

Length

Max length95
Median length35
Mean length35.27230914
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29716 ?
Unique (%)99.8%

Sample

1st rowZAKA: A Monster-Battling Card Game
2nd rowHelp Us Finish "Elevator" (A Feature Length Film)
3rd rowPHASE
4th rowPaper Dreams
5th rowThings Found on the Ground

Common Values

ValueCountFrequency (%)
Home2
 
< 0.1%
Car Wash (Canceled)2
 
< 0.1%
The Bigger Picture2
 
< 0.1%
APEX2
 
< 0.1%
UTouch: Your protection against germs on public touchscreens (Canceled)2
 
< 0.1%
Collapse2
 
< 0.1%
INIE Belt: Smart and Fashionable Belt monitors your health (Canceled)2
 
< 0.1%
Reflections2
 
< 0.1%
University of Brighton BA Photography - Degree catalogue2
 
< 0.1%
ANIMAL2
 
< 0.1%
Other values (29741)29766
99.9%

Length

2022-03-04T13:21:54.110546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the7515
 
4.4%
7320
 
4.3%
a4167
 
2.4%
of3435
 
2.0%
and2079
 
1.2%
for1647
 
1.0%
in1489
 
0.9%
canceled1420
 
0.8%
to1338
 
0.8%
film1184
 
0.7%
Other values (29835)139021
81.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

permissions
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)8.3%
Missing29774
Missing (%)> 99.9%
Memory size232.8 KiB
[]
12 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[]
5th row[]

Common Values

ValueCountFrequency (%)
[]12
 
< 0.1%
(Missing)29774
> 99.9%

Length

2022-03-04T13:21:54.241133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-04T13:21:54.574704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
12
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pledged
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct14318
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22571.12789
Minimum0
Maximum17844938
Zeros1674
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size232.8 KiB
2022-03-04T13:21:54.649897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1355
median2314.5
Q38090
95-th percentile51344.75
Maximum17844938
Range17844938
Interquartile range (IQR)7735

Descriptive statistics

Standard deviation247331.942
Coefficient of variation (CV)10.95789024
Kurtosis2273.635669
Mean22571.12789
Median Absolute Deviation (MAD)2274.5
Skewness40.96339002
Sum672303615.3
Variance6.117308952 × 1010
MonotonicityNot monotonic
2022-03-04T13:21:54.775062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01674
 
5.6%
1654
 
2.2%
10180
 
0.6%
2156
 
0.5%
5123
 
0.4%
50112
 
0.4%
25111
 
0.4%
100107
 
0.4%
2087
 
0.3%
681
 
0.3%
Other values (14308)26501
89.0%
ValueCountFrequency (%)
01674
5.6%
1654
 
2.2%
1.111
 
< 0.1%
1.2911
 
< 0.1%
1.3213
 
< 0.1%
1.426
 
< 0.1%
1.51
 
< 0.1%
2156
 
0.5%
2.011
 
< 0.1%
2.293
 
< 0.1%
ValueCountFrequency (%)
178449381
< 0.1%
17070638.41
< 0.1%
126911711
< 0.1%
114392751
< 0.1%
8596474.581
< 0.1%
78197691
< 0.1%
6840648.091
< 0.1%
65232971
< 0.1%
63187001
< 0.1%
61992491
< 0.1%

slug
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct29786
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size232.8 KiB
zaka
 
1
laserwand-magic-game
 
1
heart-and-sole-pin-badge
 
1
its-always-you
 
1
lyra-a-sci-fi-feature-film
 
1
Other values (29781)
29781 

Length

Max length63
Median length32
Mean length32.35875915
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29786 ?
Unique (%)100.0%

Sample

1st rowzaka
2nd rowhelp-us-finish-elevator-a-feature-length-film
3rd rowphase
4th rowpaper-dreams
5th rowthings-found-on-the-ground

Common Values

ValueCountFrequency (%)
zaka1
 
< 0.1%
laserwand-magic-game1
 
< 0.1%
heart-and-sole-pin-badge1
 
< 0.1%
its-always-you1
 
< 0.1%
lyra-a-sci-fi-feature-film1
 
< 0.1%
audition-one-hundred-actors-one-love-story-01
 
< 0.1%
gobbins-token-cards-by-mac1
 
< 0.1%
zero-point-game-console-upgrade-to-the-future1
 
< 0.1%
the-perfect-match-for-luxury-watches-bezel-tennis1
 
< 0.1%
dante-brown-warehouse-dance-2017-season-launch1
 
< 0.1%
Other values (29776)29776
> 99.9%

Length

2022-03-04T13:21:54.935061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zaka1
 
< 0.1%
pulse-dance-project-spring-showcase-20141
 
< 0.1%
things-found-on-the-ground1
 
< 0.1%
last-contact1
 
< 0.1%
creative-control1
 
< 0.1%
humanity-a-halo-fan-film1
 
< 0.1%
shadow-tactics-the-board-game-solo-co-op-ronin-expansion1
 
< 0.1%
beautiful-things-01
 
< 0.1%
burbank-high-school-color-guard-needs-a-new-floor1
 
< 0.1%
vermin-vendetta1
 
< 0.1%
Other values (29776)29776
> 99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

source_url
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size232.8 KiB
https://www.kickstarter.com/discover/categories/comics/graphic%20novels
2400 
https://www.kickstarter.com/discover/categories/music/classical%20music
2400 
https://www.kickstarter.com/discover/categories/film%20&%20video/narrative%20film
2400 
https://www.kickstarter.com/discover/categories/games
2395 
https://www.kickstarter.com/discover/categories/art
2384 
Other values (17)
17807 

Length

Max length82
Median length67
Mean length65.73547976
Min length51

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttps://www.kickstarter.com/discover/categories/games
2nd rowhttps://www.kickstarter.com/discover/categories/film%20&%20video/narrative%20film
3rd rowhttps://www.kickstarter.com/discover/categories/dance
4th rowhttps://www.kickstarter.com/discover/categories/film%20&%20video/romance
5th rowhttps://www.kickstarter.com/discover/categories/film%20&%20video/narrative%20film

Common Values

ValueCountFrequency (%)
https://www.kickstarter.com/discover/categories/comics/graphic%20novels2400
 
8.1%
https://www.kickstarter.com/discover/categories/music/classical%20music2400
 
8.1%
https://www.kickstarter.com/discover/categories/film%20&%20video/narrative%20film2400
 
8.1%
https://www.kickstarter.com/discover/categories/games2395
 
8.0%
https://www.kickstarter.com/discover/categories/art2384
 
8.0%
https://www.kickstarter.com/discover/categories/photography2145
 
7.2%
https://www.kickstarter.com/discover/categories/fashion/jewelry1998
 
6.7%
https://www.kickstarter.com/discover/categories/dance1965
 
6.6%
https://www.kickstarter.com/discover/categories/technology/wearables1898
 
6.4%
https://www.kickstarter.com/discover/categories/publishing/poetry1816
 
6.1%
Other values (12)7985
26.8%

Length

2022-03-04T13:21:55.061422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.kickstarter.com/discover/categories/comics/graphic%20novels2400
 
8.1%
https://www.kickstarter.com/discover/categories/music/classical%20music2400
 
8.1%
https://www.kickstarter.com/discover/categories/film%20&%20video/narrative%20film2400
 
8.1%
https://www.kickstarter.com/discover/categories/games2395
 
8.0%
https://www.kickstarter.com/discover/categories/art2384
 
8.0%
https://www.kickstarter.com/discover/categories/photography2145
 
7.2%
https://www.kickstarter.com/discover/categories/fashion/jewelry1998
 
6.7%
https://www.kickstarter.com/discover/categories/dance1965
 
6.6%
https://www.kickstarter.com/discover/categories/technology/wearables1898
 
6.4%
https://www.kickstarter.com/discover/categories/publishing/poetry1816
 
6.1%
Other values (12)7985
26.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

spotlight
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
True
19191 
False
10595 
ValueCountFrequency (%)
True19191
64.4%
False10595
35.6%
2022-03-04T13:21:55.132535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

staff_pick
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
False
25011 
True
4775 
ValueCountFrequency (%)
False25011
84.0%
True4775
 
16.0%
2022-03-04T13:21:55.171840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

state
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.8 KiB
successful
19191 
failed
7799 
canceled
 
1609
live
 
1187

Length

Max length10
Median length10
Mean length8.605519372
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlive
2nd rowsuccessful
3rd rowsuccessful
4th rowsuccessful
5th rowsuccessful

Common Values

ValueCountFrequency (%)
successful19191
64.4%
failed7799
26.2%
canceled1609
 
5.4%
live1187
 
4.0%

Length

2022-03-04T13:21:55.245222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-04T13:21:55.313770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
successful19191
64.4%
failed7799
26.2%
canceled1609
 
5.4%
live1187
 
4.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

state_changed_at
Categorical

HIGH CARDINALITY
UNIFORM

Distinct29202
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size232.8 KiB
2021-05-01 03:59:00
 
8
2021-01-01 04:59:00
 
6
2020-11-03 14:16:24
 
5
2021-03-25 22:00:00
 
5
2021-05-01 01:00:00
 
5
Other values (29197)
29757 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28709 ?
Unique (%)96.4%

Sample

1st row2021-06-14 02:00:05
2nd row2012-04-02 06:59:00
3rd row2016-02-13 18:34:51
4th row2013-03-14 12:18:28
5th row2012-04-01 04:59:03

Common Values

ValueCountFrequency (%)
2021-05-01 03:59:008
 
< 0.1%
2021-01-01 04:59:006
 
< 0.1%
2020-11-03 14:16:245
 
< 0.1%
2021-03-25 22:00:005
 
< 0.1%
2021-05-01 01:00:005
 
< 0.1%
2021-02-28 23:59:005
 
< 0.1%
2021-03-01 04:59:005
 
< 0.1%
2019-06-01 03:59:024
 
< 0.1%
2021-03-01 05:59:004
 
< 0.1%
2019-07-04 18:00:014
 
< 0.1%
Other values (29192)29735
99.8%

Length

2022-03-04T13:21:55.396177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
03:59:00372
 
0.6%
04:59:00342
 
0.6%
06:59:00185
 
0.3%
2021-06-01156
 
0.3%
04:00:00147
 
0.2%
02:00:00142
 
0.2%
01:00:00140
 
0.2%
04:59:01127
 
0.2%
03:00:00123
 
0.2%
00:00:00123
 
0.2%
Other values (22887)57715
96.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

static_usd_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5520
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.01351032
Minimum0.00878181
Maximum1.71557485
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.8 KiB
2022-03-04T13:21:55.534728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.00878181
5-th percentile0.7467844425
Q11
median1
Q31
95-th percentile1.39559745
Maximum1.71557485
Range1.70679304
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2320002093
Coefficient of variation (CV)0.2289075944
Kurtosis7.057910995
Mean1.01351032
Median Absolute Deviation (MAD)0
Skewness-1.393654869
Sum30188.4184
Variance0.0538240971
MonotonicityNot monotonic
2022-03-04T13:21:55.701569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120551
69.0%
1.209952122
 
0.1%
1.2198915522
 
0.1%
1.2072104317
 
0.1%
1.3732623117
 
0.1%
1.4196559916
 
0.1%
1.4143831514
 
< 0.1%
1.4086768914
 
< 0.1%
1.3924997213
 
< 0.1%
1.2132050113
 
< 0.1%
Other values (5510)9087
30.5%
ValueCountFrequency (%)
0.008781811
< 0.1%
0.008784881
< 0.1%
0.008847571
< 0.1%
0.008853441
< 0.1%
0.008870531
< 0.1%
0.00890811
< 0.1%
0.008908521
< 0.1%
0.008931941
< 0.1%
0.008960791
< 0.1%
0.00897461
< 0.1%
ValueCountFrequency (%)
1.715574852
 
< 0.1%
1.714465987
< 0.1%
1.71397232
 
< 0.1%
1.713652151
 
< 0.1%
1.713370294
< 0.1%
1.71286551
 
< 0.1%
1.712818564
< 0.1%
1.712114754
< 0.1%
1.711777722
 
< 0.1%
1.709515682
 
< 0.1%

usd_exchange_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5389
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.013232074
Minimum0.00877941
Maximum1.71640818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.8 KiB
2022-03-04T13:21:55.874940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.00877941
5-th percentile0.7474763325
Q11
median1
Q31
95-th percentile1.39919434
Maximum1.71640818
Range1.70762877
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2317514516
Coefficient of variation (CV)0.2287249462
Kurtosis7.055784495
Mean1.013232074
Median Absolute Deviation (MAD)0
Skewness-1.408886846
Sum30180.13055
Variance0.05370873531
MonotonicityNot monotonic
2022-03-04T13:21:56.020975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120551
69.0%
1.19937728212
 
0.7%
1.39919434149
 
0.5%
0.814559180
 
0.3%
0.7626549227
 
0.1%
0.0491123316
 
0.1%
1.3772226715
 
0.1%
0.1287987614
 
< 0.1%
1.2122799113
 
< 0.1%
1.2166545412
 
< 0.1%
Other values (5379)8697
29.2%
ValueCountFrequency (%)
0.008779411
< 0.1%
0.0087931
< 0.1%
0.008813061
< 0.1%
0.008908521
< 0.1%
0.008919371
< 0.1%
0.00893541
< 0.1%
0.008941741
< 0.1%
0.008961772
< 0.1%
0.008974651
< 0.1%
0.00899561
< 0.1%
ValueCountFrequency (%)
1.716408184
< 0.1%
1.715913382
< 0.1%
1.715892772
< 0.1%
1.715574851
 
< 0.1%
1.71397231
 
< 0.1%
1.713652151
 
< 0.1%
1.71286552
< 0.1%
1.712818562
< 0.1%
1.712114751
 
< 0.1%
1.711777721
 
< 0.1%

usd_pledged
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct19229
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15804.41773
Minimum0
Maximum8596474.58
Zeros1674
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size232.8 KiB
2022-03-04T13:21:56.186693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1351
median2265.815216
Q37701.211713
95-th percentile43269.06162
Maximum8596474.58
Range8596474.58
Interquartile range (IQR)7350.211713

Descriptive statistics

Standard deviation132920.7538
Coefficient of variation (CV)8.410354375
Kurtosis1702.426275
Mean15804.41773
Median Absolute Deviation (MAD)2224.890469
Skewness35.523334
Sum470750386.5
Variance1.766792679 × 1010
MonotonicityNot monotonic
2022-03-04T13:21:56.336625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01674
 
5.6%
1432
 
1.5%
10116
 
0.4%
2101
 
0.3%
5089
 
0.3%
2583
 
0.3%
579
 
0.3%
10076
 
0.3%
3062
 
0.2%
657
 
0.2%
Other values (19219)27017
90.7%
ValueCountFrequency (%)
01674
5.6%
0.48893741
 
< 0.1%
0.51104771
 
< 0.1%
0.52156041
 
< 0.1%
0.53210311
 
< 0.1%
0.53755421
 
< 0.1%
0.584515551
 
< 0.1%
0.58493071
 
< 0.1%
0.587596451
 
< 0.1%
0.60613021
 
< 0.1%
ValueCountFrequency (%)
8596474.581
< 0.1%
8332853.931
< 0.1%
59880891
< 0.1%
5702153.381
< 0.1%
5333792.841
< 0.1%
4831975.871
< 0.1%
4770033.8181
< 0.1%
4681636.971
< 0.1%
3502960.391
< 0.1%
3310872.981
< 0.1%

usd_type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing83
Missing (%)0.3%
Memory size232.8 KiB
international
29691 
domestic
 
12

Length

Max length13
Median length13
Mean length12.99798
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowinternational
2nd rowinternational
3rd rowinternational
4th rowinternational
5th rowinternational

Common Values

ValueCountFrequency (%)
international29691
99.7%
domestic12
 
< 0.1%
(Missing)83
 
0.3%

Length

2022-03-04T13:21:56.463435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-04T13:21:56.537533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
international29691
> 99.9%
domestic12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

location_city
Categorical

HIGH CARDINALITY

Distinct4279
Distinct (%)14.4%
Missing49
Missing (%)0.2%
Memory size232.8 KiB
Los Angeles
 
1459
New York
 
1422
London
 
1211
Chicago
 
576
Brooklyn
 
576
Other values (4274)
24493 

Length

Max length39
Median length8
Mean length8.596327807
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2368 ?
Unique (%)8.0%

Sample

1st rowMelbourne
2nd rowArcadia
3rd rowBoulder
4th rowBrooklyn
5th rowMadison

Common Values

ValueCountFrequency (%)
Los Angeles1459
 
4.9%
New York1422
 
4.8%
London1211
 
4.1%
Chicago576
 
1.9%
Brooklyn576
 
1.9%
San Francisco482
 
1.6%
Portland380
 
1.3%
Seattle378
 
1.3%
Toronto360
 
1.2%
Austin332
 
1.1%
Other values (4269)22561
75.7%

Length

2022-03-04T13:21:56.632188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new1608
 
4.2%
los1483
 
3.9%
angeles1468
 
3.8%
york1434
 
3.7%
london1261
 
3.3%
san910
 
2.4%
city653
 
1.7%
chicago577
 
1.5%
brooklyn576
 
1.5%
francisco484
 
1.3%
Other values (4207)27933
72.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

location_state
Categorical

HIGH CARDINALITY

Distinct621
Distinct (%)2.1%
Missing65
Missing (%)0.2%
Memory size232.8 KiB
CA
3756 
England
2951 
NY
2850 
TX
 
1095
FL
 
904
Other values (616)
18165 

Length

Max length49
Median length2
Mean length4.041586757
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique209 ?
Unique (%)0.7%

Sample

1st rowVIC
2nd rowCA
3rd rowCO
4th rowNY
5th rowWI

Common Values

ValueCountFrequency (%)
CA3756
 
12.6%
England2951
 
9.9%
NY2850
 
9.6%
TX1095
 
3.7%
FL904
 
3.0%
IL754
 
2.5%
WA685
 
2.3%
PA655
 
2.2%
MA626
 
2.1%
ON598
 
2.0%
Other values (611)14847
49.8%

Length

2022-03-04T13:21:56.757309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca3756
 
11.7%
england2951
 
9.2%
ny2850
 
8.9%
tx1095
 
3.4%
fl904
 
2.8%
il754
 
2.3%
wa685
 
2.1%
pa655
 
2.0%
ma626
 
2.0%
on598
 
1.9%
Other values (714)17225
53.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-03-04T13:21:46.647465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:34.504858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:36.010217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:37.232662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:38.427466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:39.831869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:41.306570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:42.524940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:43.840762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:45.118377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:46.757092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:34.656574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:36.136695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:37.354268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:38.567136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:39.959244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:41.434133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:42.659888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:43.967004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:45.516737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:46.869861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:34.788907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:36.252885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:37.476942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:38.701375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:40.081711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:41.553590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:42.780950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:44.085252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:45.633487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:46.988302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:34.912811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:36.366911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:37.599881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:38.844647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:40.200598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:41.665808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:42.904158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:44.210932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:45.754769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:47.119657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:35.045458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:36.500747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:37.722239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:39.000178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:40.578367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:41.785259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:43.038204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:44.348870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:45.888264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:47.258570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:35.168969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:36.620610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:37.838812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:39.141819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:40.698209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:41.906765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:43.172546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:44.480571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:46.019417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:47.394068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:35.295924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:36.733897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:37.954213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:39.282140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:40.816705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:42.028293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:43.298605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:44.604806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:46.139277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:47.529483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:35.623839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:36.866026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:38.076673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:39.440936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:40.940979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:42.160304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:43.441010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:44.730851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:46.271500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:47.673475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:35.750200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:36.986990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:38.194249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:39.581825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:41.064728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:42.289053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:43.581978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:44.863264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:46.404819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:47.813650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:35.884834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:37.115323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:38.314461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:39.710709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:41.190725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:42.415081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:43.717386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:44.997483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-04T13:21:46.531610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-04T13:21:56.867770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-04T13:21:57.076315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-04T13:21:57.285089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-04T13:21:57.496722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-04T13:21:57.981186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-04T13:21:48.196174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-04T13:21:49.311442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-04T13:21:49.910066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-04T13:21:50.199542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0backers_countblurbcategoryconverted_pledged_amountcountrycountry_displayable_namecreated_atcreatorcurrencycurrency_symbolcurrency_trailing_codecurrent_currencydeadlinedisable_communicationfriendsfx_rategoalidis_backingis_starrableis_starredlaunched_atnamepermissionspledgedslugsource_urlspotlightstaff_pickstatestate_changed_atstatic_usd_rateusd_exchange_rateusd_pledgedusd_typelocation_citylocation_state
0065A super-cheeky strategy card game that's full of monsters, magic and lots of sneaky revenge!Tabletop Games2856AUAustralia2021-05-21 01:00:51Jof CroxfordAUD$TrueUSD2021-07-14 02:00:04FalseNaN0.76265515000.02128867236NaNTrueNaN2021-06-14 02:00:04ZAKA: A Monster-Battling Card GameNaN3745.32zakahttps://www.kickstarter.com/discover/categories/gamesFalseFalselive2021-06-14 02:00:050.7707980.7626552886.886851internationalMelbourneVIC
11146Help us finish a feature film about two strangers stuck in adjacent elevators that share a conversation that may alter their realities.Narrative Film10120USthe United States2012-01-26 18:20:01Nine Finger ProductionsUSD$TrueUSD2012-04-02 06:59:00FalseNaN1.00000010000.0522081392NaNFalseNaN2012-02-12 20:00:26Help Us Finish "Elevator" (A Feature Length Film)NaN10120.00help-us-finish-elevator-a-feature-length-filmhttps://www.kickstarter.com/discover/categories/film%20&%20video/narrative%20filmTrueFalsesuccessful2012-04-02 06:59:001.0000001.00000010120.000000internationalArcadiaCA
2241Considered playfully mischievous and outlaws in the world of movement, Rogue Co. brings you their 5th full length production, "PHASE."Performances3785USthe United States2015-11-13 21:04:33Rogue Co.USD$TrueUSD2016-02-13 18:34:51FalseNaN1.0000003000.0688419156NaNFalseNaN2016-01-14 18:34:51PHASENaN3785.00phasehttps://www.kickstarter.com/discover/categories/danceTrueFalsesuccessful2016-02-13 18:34:511.0000001.0000003785.000000internationalBoulderCO
33466A fantastical love story about two New Yorkers whose relationship unfolds aboard a paper mache subway!Romance21634USthe United States2013-02-01 17:22:07Ryan Mitchel and TeamUSD$TrueUSD2013-03-14 12:18:28FalseNaN1.00000020000.01395612011NaNFalseNaN2013-02-12 13:18:28Paper DreamsNaN21634.00paper-dreamshttps://www.kickstarter.com/discover/categories/film%20&%20video/romanceTrueTruesuccessful2013-03-14 12:18:281.0000001.00000021634.000000internationalBrooklynNY
4470A story about a girl named Jane who is learning to accept human frailty as she deals with the news of her sister getting knocked up.Narrative Film3081USthe United States2012-02-23 19:45:00Carol BrandtUSD$TrueUSD2012-04-01 04:59:00FalseNaN1.0000003000.01895670076NaNFalseNaN2012-03-02 13:20:47Things Found on the GroundNaN3081.00things-found-on-the-groundhttps://www.kickstarter.com/discover/categories/film%20&%20video/narrative%20filmTrueFalsesuccessful2012-04-01 04:59:031.0000001.0000003081.000000internationalMadisonWI
55166A short film portraying the despair of a researcher long after his last contact with the outside worldScience Fiction6115GBthe United Kingdom2014-08-20 12:09:28Michael GarrettGBP£FalseUSD2014-11-15 16:25:29FalseNaN1.3991943000.0273779926NaNFalseNaN2014-10-16 15:25:29LAST CONTACTNaN3903.00last-contacthttps://www.kickstarter.com/discover/categories/film%20&%20video/science%20fictionTrueFalsesuccessful2014-11-15 16:25:291.5897541.5669736204.811228internationalShipkaStara Zagora
66318A NEAR FUTURE feature film about sex, technology, and obsession.Narrative Film32001USthe United States2014-04-08 17:52:24Benjamin DickinsonUSD$TrueUSD2014-06-01 03:59:00FalseNaN1.00000030000.01905826891NaNFalseNaN2014-04-30 15:46:18CREATIVE CONTROLNaN32001.46creative-controlhttps://www.kickstarter.com/discover/categories/film%20&%20video/narrative%20filmTrueTruesuccessful2014-06-01 03:59:021.0000001.00000032001.460000internationalBrooklynNY
7727A Halo Fan Film about Master Chief after Halo 4 & Halo Escalations: The Next 72 Hours.Science Fiction1391USthe United States2015-01-13 05:58:39Robert J MerrittUSD$TrueUSD2015-07-09 19:52:57FalseNaN1.00000070000.01606387274NaNFalseNaN2015-06-08 19:52:57Humanity: A Halo Fan FilmNaN1391.00humanity-a-halo-fan-filmhttps://www.kickstarter.com/discover/categories/film%20&%20video/science%20fictionFalseFalsefailed2015-07-09 19:52:571.0000001.0000001391.000000internationalVancouverBC
88638Tactical stealth game with secret action programming for 1-6 players. Now with 1 vs. many and cooperative/solo modes.Tabletop Games56758NLthe Netherlands2020-10-13 14:27:30Antler GamesEURFalseUSD2021-03-03 20:00:00FalseNaN1.1993775000.01461979375NaNFalseNaN2021-02-16 16:06:13Shadow Tactics: the Board Game + solo/co-op Ronin expansionNaN46952.50shadow-tactics-the-board-game-solo-co-op-ronin-expansionhttps://www.kickstarter.com/discover/categories/gamesTrueFalsesuccessful2021-03-03 20:00:011.2132051.20885256963.008232internationalBudapestBudapest
9980A queer love story touched by motherhood and self-doubt with a Brooklyn backdrop.Romance4215USthe United States2016-12-30 19:27:19Paige PolkUSD$TrueUSD2017-02-27 15:00:00FalseNaN1.00000024953.01351145783NaNFalseNaN2017-01-31 23:44:21Beautiful ThingsNaN4215.00beautiful-things-0https://www.kickstarter.com/discover/categories/film%20&%20video/romanceFalseTruefailed2017-02-27 15:00:001.0000001.0000004215.000000internationalBrooklynNY

Last rows

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29776196659I'm flying Swedish percussionist Måns Block and the Vindla String Quartet in to London for a concert where I'll be launching my new CDClassical Music1982GBthe United Kingdom2017-01-15 16:14:57Johan HugossonGBP£FalseUSD2017-03-30 20:00:00FalseNaN1.3991941500.0645573052NaNFalseNaN2017-03-01 12:30:42Bring Johan Hugosson's 'Made in Sweden' Concert to London.NaN1593.0bring-johan-hugossons-made-in-sweden-concert-to-lohttps://www.kickstarter.com/discover/categories/music/classical%20musicTrueFalsesuccessful2017-03-30 20:00:001.2432311.2442451980.466362internationalLondonEngland
29777196776The FIRST screen protector with an image that disappears when your phone is in use! Allowing for personalization and protection.Wearables8183USthe United States2014-05-04 21:07:09ScreenSwag inc.USD$TrueUSD2014-08-20 01:38:21FalseNaN1.0000008000.0274524291NaNFalseNaN2014-07-01 01:38:21ScreenSwag™ - Screen Protection with Maximum ExpressionNaN8183.0screenswag-screen-protection-with-maximum-expressihttps://www.kickstarter.com/discover/categories/technology/wearablesTrueFalsesuccessful2014-08-20 01:38:211.0000001.0000008183.000000internationalOrlandoFL
297781969209Photographs by Glen Wexler | Foreword by Billy F GibbonsPhotobooks32669USthe United States2018-10-16 01:05:39GLEN WEXLERUSD$TrueUSD2018-12-03 07:59:00FalseNaN1.00000022000.0554027675NaNFalseNaN2018-10-30 16:26:02The ’80s Portrait SessionsNaN32669.0the-80s-portrait-sessionshttps://www.kickstarter.com/discover/categories/photographyTrueTruesuccessful2018-12-03 07:59:001.0000001.00000032669.000000internationalHollywoodCA
2977919700Existential Love Poetry - A New GenrePoetry0USthe United States2012-09-29 09:33:47Erick R WilliamsUSD$TrueUSD2012-11-01 10:03:00FalseNaN1.0000001000.0323647377NaNFalseNaN2012-10-03 03:46:05Existential Love Poems - An Experiment in New PoetryNaN0.0existential-love-poems-an-experiment-in-new-poetryhttps://www.kickstarter.com/discover/categories/publishing/poetryFalseFalsecanceled2012-10-08 03:28:321.0000001.0000000.000000internationalWalkersvilleMD
297801971110RUAWALK is designed to break the boredom and maximize the effects of walking with a dynamic walking program and synchronized RUAMUSIC.Wearables28347USthe United States2017-01-24 04:54:47RUAWALK Corp.USD$TrueUSD2017-03-31 11:00:56FalseNaN1.00000030000.0457952399NaNFalseNaN2017-02-23 12:00:56RUAWALK: The First Music-Guided Walking Tempo ControllerNaN28347.0ruawalk-the-first-music-guided-walking-tempo-contrhttps://www.kickstarter.com/discover/categories/technology/wearablesFalseFalsefailed2017-03-31 11:00:561.0000001.00000028347.000000internationalLos AngelesCA
29781197248I'm creating a book to celebrate the diverse poetry of The Gloucester Poetry Society and its UK and International membersPoetry1394GBthe United Kingdom2017-08-08 13:46:35Jason ConwayGBP£FalseUSD2017-09-17 23:00:00FalseNaN1.4082051000.02127789307NaNFalseNaN2017-08-15 20:41:34Poetry Without Pretension BookNaN1026.0poetry-without-pretension-bookhttps://www.kickstarter.com/discover/categories/publishing/poetryTrueFalsesuccessful2017-09-17 23:00:001.3014991.3589821335.337646internationalGloucesterEngland
2978219730A biographical poetry book relating to the many life experiences I have had some comical some serious and some unbelievable but truePoetry0GBthe United Kingdom2015-09-22 12:19:23Keiron SaeedGBP£FalseUSD2015-10-22 12:19:00FalseNaN1.40820515000.01362143084NaNFalseNaN2015-09-22 13:59:27The Learners Guide to LifeNaN0.0the-learners-guide-to-lifehttps://www.kickstarter.com/discover/categories/publishing/poetryFalseFalsefailed2015-10-22 12:19:001.5536251.5428600.000000internationalBirminghamEngland
2978319740Je voudrais voir mon meilleur ami Pierre pour son anniversaire en début janvier.Et ma meilleur amie Cynthia pour un week-end.Nature0FRFrance2015-11-18 23:23:18Quentin MullerEURFalseUSD2016-01-18 03:38:55FalseNaN1.1993771100.01674716636NaNFalseNaN2015-11-19 03:38:55Aller voir mes 2 seuls amis en suisseNaN0.0aller-voir-mes-2-seuls-amis-en-suissehttps://www.kickstarter.com/discover/categories/photography/natureFalseFalsefailed2016-01-18 03:38:561.0660951.0913840.000000internationalLausanneCanton of Vaud
29784197649We are commissioning a new work, to debut at our final concert of the season, on May 20, 2017. We hope you'll consider supporting us!Classical Music4405USthe United States2017-02-01 03:29:57Bellingham Chamber ChoraleUSD$TrueUSD2017-04-17 01:30:00FalseNaN1.0000004000.02114005133NaNFalseNaN2017-03-06 07:51:47BCC premieres a new choral work in May 2017!NaN4405.0bcc-commissions-a-new-choral-work-premiering-in-behttps://www.kickstarter.com/discover/categories/music/classical%20musicTrueFalsesuccessful2017-04-17 01:30:001.0000001.0000004405.000000internationalBellinghamWA
2978519770Un libro su coloro che ho amato, perduto e mai dimenticato. A book about the ones that I loved, miss and never forget.Poetry0ITItaly2015-12-13 08:22:16Marco TartariEURFalseUSD2016-01-12 09:52:49FalseNaN1.2122801000.0996600987NaNFalseNaN2015-12-13 09:52:49Le braci del cielo (The cinders of sky) (Canceled)NaN0.0le-braci-del-cielo-the-cinders-of-skyhttps://www.kickstarter.com/discover/categories/publishing/poetryFalseFalsecanceled2015-12-13 19:51:481.0993021.0993020.000000internationalNaplesCampania